- Faculty
Keisuke Ejima
-
Assistant Research Scientist
Open Research and Contributor Identifier
Education
The University of Tokyo, Ph.D. in Information Science and Technology, 2014
The University of Tokyo, M.S. in Information Science and Technology, 2011
The University of Tokyo, B.S. in Economics, 2009
Background
Grant/Awards
- 2021 MIDAS COVID-19 Urgent Grant ($8,000)
- 2016 Japanese Society for Mathematical Biology Young Scholar Award
- 2016 Best Paper Award in General Statistics Research, The Science Unbound Foundation
Research Interests
I have been applying mathematical and statistical models to solve public health issues. Specifically, I am interested in two research fields: obesity/nutrition and infectious diseases. For obesity/nutrition research, my interest is in measurement error of energy intake (or energy expenditure). Energy intake can be measured by food frequency questionnaire or food diary; however, energy intake measured by those self-report basis approaches are known to be biased (because of recall bias, for example). There are a couple of methods to correct the bias due to self-reporting, and I have assessed the utility of such correction methods (Ejima et al, AJCN, 2019). I am also interested in developing statical models to adjust such biases using combination of biomarker data which capture the magnitude of bias. (Key words: multiple imputation, Bayesian model). For infectious disease research, I am leading several COVID-19 projects, focusing on biomarker dynamics at acute phase of infection. For example, viral load data continuously collected from the same person tell us a lot about not only the clinical characteristics of the disease, but epidemiological features. By analyzing the data using a mathematical model that describe viral dynamics, we found the viral load reaches peak soon after symptom onset (Kim et al, PLoS Biology, 2021), and this might be a reason why most of randomized control trials fail to find significant effect of antivirals (Iwanami et al, under review). Further, by hindcasting the viral dynamics, we found we could infer the time of infection and estimate incubation period (Ejima et al, Epidemics, 2021). Extending these COVID-19 works using the viral dynamics model, I am interested in modelling immunological response, which might help identify patients with high risk of mortality or hospitalization as early as possible.
Selected Publications
Articles
Kim KS†, K. Ejima K†, …, Perelson AS‡, and S. Iwami S‡. A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2, PLoS Biology, (Accepted) (†,‡Equal contribution)
Ejima K†*, Kim KS†, Ludema C, Bento AI, …, Iwami S*. Estimation of the incubation period of COVID-19 using viral load data, Epidemics, (Accepted) (†Equal contribution, *corresponding author)
Iwanami S†, Ejima K*†, …, Perelson AS‡, Iwami S‡*, Wakita T. Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: a modeling study combined with clinical data, (under review) (†,‡Equal contribution, *corresponding author)
Ejima K, Brown AW, Schoeller DA, Heymsfield SB, Nelson EJ, Allison DB. Does exclusion of extreme reporters of energy intake (the 'Goldberg cutoffs') reliably reduce or eliminate bias in nutrition studies? Analysis with illustrative associations of energy intake with health outcomes. Am J Clin Nutr. 2019;110(5):1231-1239
Ejima, K., Brown, A.W., Schoeller, A., Heymsfield, S.B., Nelson, E.J., Allison, D.B. (2019) Does exclusion of extreme reporters of energy intake (the 'Goldberg cutoffs') reliably reduce or eliminate bias in nutrition studies? Analysis with illustrative associations of energy intake with health outcomes. Am J Clin Nutr. (Accepted)
Ejima, K., Thomas, D., Allison, D.B. (2018) A Mathematical Model for Predicting Obesity Transmission With Both Genetic and Nongenetic Heredity. Obesity. 26(5):927-933
Ejima, K., Pavela, G., Li, P., Allison, D.B. (2018) Flexibly Generalized lambda distribution for flexibly testing differences beyond the mean in the distribution of a dependent variable such as body mass index. International Journal of Obesity. 42(4):930-933
Ejima, K., Li, P., Smith Jr, D.L., Nagy, T.R., Kadish, I., van Groen, T., Dawson, J.A., Yang, Y., Patki, A., Allison, D.B. (2016) Observational Research Rigor Alone Does Not Justify Causal Inference, Eur J Clin Invest. 46(12):985-993.